Library loading

import scanpy as sc, anndata as ad, numpy as np, pandas as pd,  scvelo as scv
from scipy import sparse
from anndata import AnnData
import warnings
import socket
import holoviews as hv
import plotly.express as px

from matplotlib import pylab
import sys
import yaml
import os
from pandas.api.types import CategoricalDtype
import plotly



import matplotlib.pyplot as plt

warnings.filterwarnings('ignore')

plotly.offline.init_notebook_mode()

import ipynbname
nb_fname = ipynbname.name()

%matplotlib inline

sc.settings.verbosity = 3         # verbosity: errors (0), warnings (1), info (2), hints (3)
sc.logging.print_header()
sc.settings.set_figure_params(dpi=50, facecolor='white')
pylab.rcParams['figure.figsize'] = (9, 9)
outBaseName = "04.1C_Cycling_DA"
figDir = "./figures"

scanpy==1.8.1 anndata==0.7.6 umap==0.4.6 numpy==1.20.2 scipy==1.6.3 pandas==1.2.4 scikit-learn==0.24.2 statsmodels==0.13.1 python-igraph==0.9.8 louvain==0.7.1 pynndescent==0.5.5

Configure paths

hostRoot = "-".join(socket.gethostname().split('-')[0:2])

with open(os.path.expanduser('~')+"/paths_config.yaml", 'r') as f:
    paths = yaml.load(f, Loader=yaml.FullLoader)

#indir=paths["paths"]["indir"][hostRoot]
outdir="./outdir"
FinalLeaf="/Cycling"
#projectBaseDir=paths["paths"]["projectBaseDir"][hostRoot]
with open("./colorMap.yaml", 'r') as f:
    colorMap = yaml.load(f, Loader=yaml.FullLoader)["uns_colors"]
colorMap

{'medial': {'color': '#CD5C5C'},
 'distal': {'color': '#FFCBCB'},
 'proximal': {'color': '#8D021F'},
 'piece1': {'color': '#281E5D'},
 'piece2': {'color': '#3779FF'},
 'piece3': {'color': '#BFD4FF'},
 'control': {'color': '#0056D1'},
 'polaroid': {'color': '#DE001E'},
 'enriched': {'color': '#DE001E'},
 'not_enriched': {'color': '#0056D1'},
 'pfc': {'color': '#DE001E'},
 'somatosensory': {'color': '#E5E4E2'},
 'temporal': {'color': '#0056D1'},
 'motor': {'color': '#37F7C8'},
 'v1': {'color': '#28F30C'},
 'parietal': {'color': '#D41FFC'}}

adataAll = sc.read_h5ad(outdir+"/3_polaroid_quickAnno.h5ad")

scv.tl.score_genes_cell_cycle(adataAll)

calculating cell cycle phase
computing score 'S_score'
    finished: added
    'S_score', score of gene set (adata.obs).
    543 total control genes are used. (0:00:01)
computing score 'G2M_score'
    finished: added
    'G2M_score', score of gene set (adata.obs).
    415 total control genes are used. (0:00:01)
-->     'S_score' and 'G2M_score', scores of cell cycle phases (adata.obs)

sc.pl.umap(adataAll, color=["leiden0.3","phase"] ,size = 30, add_outline = True,outline_width=(0.2, 0.05), frameon=False)

... storing 'phase' as categorical

UMAP

adataAll.obs["Progenitor"] = "Other"
adataAll.obs.loc[(adataAll.obs["leiden0.3"].isin(["5","6","4"])) & adataAll.obs["phase"].isin(["G2M","S"]),"Progenitor"] = "CyclingProgenitor"
adataAll.obs["Progenitor"]
sc.pl.umap(adataAll, color="Progenitor", groups=["CyclingProgenitor"] ,size = 30, add_outline = True,outline_width=(0.2, 0.05), frameon=False)

... storing 'Progenitor' as categorical

go back to rawdata and clean

adata = adataAll.raw.to_adata().copy()

del adata.var["highly_variable"]
adata.obs = adataAll[adata.obs_names].obs.copy()
del adata.uns
del adata.obsm
adata = adata[adata.obs["Progenitor"] == "CyclingProgenitor"]

for md in [md for md in adata.obs.columns if "leiden" in md and md != "leiden0.3"]:
    
    del adata.obs[md]

with open("./colorMap.yaml", 'r') as f:
    colorMap = yaml.load(f, Loader=yaml.FullLoader)["uns_colors"]
colorMap

adata.uns["regionContrast_colors"] = [colorMap[i]["color"] for i in adata.obs["regionContrast"].cat.categories]
adata.uns["region_colors"] = [colorMap[i]["color"] for i in adata.obs["region"].cat.categories]
adata.uns["type_colors"] = [colorMap[i]["color"] for i in adata.obs["type"].cat.categories]

Trying to set attribute `.uns` of view, copying.

adata

AnnData object with n_obs × n_vars = 2456 × 28371
    obs: 'dataset', 'organoid', 'region', 'type', 'type_region', 'regionContrast', 'n_genes_by_counts', 'log1p_n_genes_by_counts', 'total_counts', 'log1p_total_counts', 'total_counts_mt', 'log1p_total_counts_mt', 'pct_counts_mt', 'total_counts_ribo', 'log1p_total_counts_ribo', 'pct_counts_ribo', 'n_genes', 'is.Stressed', 'leiden0.3', 'S_score', 'G2M_score', 'phase', 'Progenitor'
    var: 'mt', 'ribo', 'n_cells_by_counts', 'mean_counts', 'log1p_mean_counts', 'pct_dropout_by_counts', 'total_counts', 'log1p_total_counts', 'n_cells'
    uns: 'regionContrast_colors', 'region_colors', 'type_colors'

Sankey of leiden per type

obsTupleToMap = ("region","leiden0.3")
SankeyDF=adata.obs[list(obsTupleToMap)]
SankeyDF.columns = ["region","leiden0.3"]
SankeyDF = SankeyDF.groupby(['region','leiden0.3']).size().reset_index().rename(columns={0:'count'})
SankeyDF=SankeyDF[SankeyDF["count"] != 0]
hv.extension('bokeh')



sankey1 = hv.Sankey(SankeyDF, kdims=["region", "leiden0.3"], vdims="count")



sankey1.opts(cmap='Colorblind',label_position='left',
edge_color='region', edge_line_width=0,
node_alpha=1.0, node_width=40, node_sort=True,
width=900, height=700, bgcolor="snow")

Re HVGs

Vertical intersection

# Given the epxloratory phase as many genes were retained
VERTICAL_HVGs = {}
for group in adata.obs.type_region.unique():
    adata_group = adata[adata.obs.type_region == group].copy()
    HVGsDF = sc.pp.highly_variable_genes(adata_group, min_mean=0.0125, max_mean=3, min_disp=0.5, inplace=False, batch_key="organoid")
    VERTICAL_HVGs[group]  = set(HVGsDF[HVGsDF["highly_variable_nbatches"] == 3].index)
    
VERTICAL_HVGs = set.union(*list(VERTICAL_HVGs.values()))

extracting highly variable genes
    finished (0:00:01)
extracting highly variable genes
    finished (0:00:01)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)
extracting highly variable genes
    finished (0:00:00)

len(VERTICAL_HVGs)

2111

Horizontal intersection

import itertools

# Setting up contrasts
proximal = ["polaroid1_proximal","polaroid2_proximal","polaroid3_proximal"]
medial = ["polaroid1_medial","polaroid2_medial","polaroid3_medial"]
distal = ["polaroid1_distal","polaroid2_distal","polaroid3_distal"]

p1 = ["control1_piece1","control2_piece1","control3_piece1"]
p2 = ["control1_piece2","control2_piece2","control3_piece2"]
p3 = ["control1_piece3","control2_piece3","control3_piece3"]

proximal_vs_medial  = list(itertools.product(proximal, medial))
medial_vs_distal  = list(itertools.product(medial, distal))

p1_vs_p2 = list(itertools.product(p1, p2))
p2_vs_p3 = list(itertools.product(p2, p3))

HORIZONTAL_HVGs = {}


# Proximal vs distal regions
# Proximal vs distal regions
# Proximal vs distal regions

proximal_vs_medial_HVGs = {}

for contrast in proximal_vs_medial:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())

    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    proximal_vs_medial_HVGs["_".join(contrast)] = HVGs

proximal_vs_medial_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(proximal_vs_medial_HVGs.values())])))
proximal_vs_medial_HVGs = set(proximal_vs_medial_HVGs.value_counts()[proximal_vs_medial_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["proximal_vs_medial_HVGs"] = proximal_vs_medial_HVGs

    
# medial vs distal regions
# medial vs distal regions
# medial vs distal regions

medial_vs_distal_HVGs = {}

for contrast in medial_vs_distal:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())

    
    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    medial_vs_distal_HVGs["_".join(contrast)] = HVGs

medial_vs_distal_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(medial_vs_distal_HVGs.values())])))
medial_vs_distal_HVGs = set(medial_vs_distal_HVGs.value_counts()[medial_vs_distal_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["medial_vs_distal_HVGs"] = medial_vs_distal_HVGs


# P1 vs P2
# P1 vs P2
# P1 vs P2

p1_vs_p2_HVGs = {}

for contrast in p1_vs_p2:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())
    
    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    p1_vs_p2_HVGs["_".join(contrast)] = HVGs

p1_vs_p2_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(p1_vs_p2_HVGs.values())])))
p1_vs_p2_HVGs = set(p1_vs_p2_HVGs.value_counts()[p1_vs_p2_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["p1_vs_p2_HVGs"] = p1_vs_p2_HVGs



# P2 vs P3
# P2 vs P3
# P2 vs P3

p2_vs_p3_HVGs = {}

for contrast in p2_vs_p3:
    
    adataContrast = adata[adata.obs["dataset"].isin(list(contrast))].copy()
    print(adataContrast.obs.dataset.value_counts())

    sc.pp.highly_variable_genes(adataContrast, min_mean=0.0125, max_mean=3, min_disp=0.5)
    HVGs = set(adataContrast.var[adataContrast.var.highly_variable].index.tolist())
    p2_vs_p3_HVGs["_".join(contrast)] = HVGs

p2_vs_p3_HVGs = pd.Series(list(itertools.chain.from_iterable([list(i) for i in list(p2_vs_p3_HVGs.values())])))
p2_vs_p3_HVGs = set(p2_vs_p3_HVGs.value_counts()[p2_vs_p3_HVGs.value_counts() == 9].index.tolist())
HORIZONTAL_HVGs["p2_vs_p3_HVGs"] = p2_vs_p3_HVGs

polaroid1_proximal    496
polaroid1_medial       84
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_proximal    496
polaroid2_medial       33
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_proximal    496
polaroid3_medial       28
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_proximal    531
polaroid1_medial       84
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_proximal    531
polaroid2_medial       33
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_proximal    531
polaroid3_medial       28
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_proximal    367
polaroid1_medial       84
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_proximal    367
polaroid2_medial       33
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_proximal    367
polaroid3_medial       28
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_distal    242
polaroid1_medial     84
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_medial    84
polaroid2_distal    55
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_medial    84
polaroid3_distal    45
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_distal    242
polaroid2_medial     33
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_distal    55
polaroid2_medial    33
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_distal    45
polaroid2_medial    33
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid1_distal    242
polaroid3_medial     28
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid2_distal    55
polaroid3_medial    28
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
polaroid3_distal    45
polaroid3_medial    28
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece2    58
control1_piece1    37
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece2    39
control1_piece1    37
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    99
control1_piece1    37
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece1    75
control1_piece2    58
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece1    75
control2_piece2    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    99
control2_piece1    75
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece1    93
control1_piece2    58
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece1    93
control2_piece2    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    99
control3_piece1    93
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece2    58
control1_piece3    56
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece2    58
control2_piece3    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece3    79
control1_piece2    58
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control1_piece3    56
control2_piece2    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control2_piece2    39
control2_piece3    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece3    79
control2_piece2    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    99
control1_piece3    56
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    99
control2_piece3    39
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)
control3_piece2    99
control3_piece3    79
Name: dataset, dtype: int64
extracting highly variable genes
    finished (0:00:00)
--> added
    'highly_variable', boolean vector (adata.var)
    'means', float vector (adata.var)
    'dispersions', float vector (adata.var)
    'dispersions_norm', float vector (adata.var)

Combine all HVGs

HORIZONTAL_HVGs = set.union(*list(HORIZONTAL_HVGs.values()))

JointHVGs = list(HORIZONTAL_HVGs.union(VERTICAL_HVGs))

adata.var["highly_variable"] = adata.var_names.isin(JointHVGs)

adata.var["highly_variable"].sum()

2121

Dataset Preprocessing

Neighbors

sc.pp.pca(adata, use_highly_variable=True)

computing PCA
    on highly variable genes
    with n_comps=50
    finished (0:00:01)

sc.pp.neighbors(adata, n_neighbors=50, n_pcs=10)

computing neighbors
    using 'X_pca' with n_pcs = 10
    finished: added to `.uns['neighbors']`
    `.obsp['distances']`, distances for each pair of neighbors
    `.obsp['connectivities']`, weighted adjacency matrix (0:00:02)

sc.tl.leiden(adata, resolution=1, key_added="subLeiden")

running Leiden clustering
    finished: found 13 clusters and added
    'subLeiden', the cluster labels (adata.obs, categorical) (0:00:00)

sc.tl.umap(adata)

computing UMAP
    finished: added
    'X_umap', UMAP coordinates (adata.obsm) (0:00:05)

sc.tl.draw_graph(adata, n_jobs=4)

drawing single-cell graph using layout 'fa'
    finished: added
    'X_draw_graph_fa', graph_drawing coordinates (adata.obsm) (0:00:11)

sc.tl.diffmap(adata)

computing Diffusion Maps using n_comps=15(=n_dcs)
computing transitions
    finished (0:00:00)
    eigenvalues of transition matrix
    [1.         0.97674865 0.9594159  0.9378521  0.9327409  0.9223122
     0.91046554 0.9039473  0.8965334  0.8922645  0.887295   0.88147134
     0.86919874 0.86007625 0.8424786 ]
    finished: added
    'X_diffmap', diffmap coordinates (adata.obsm)
    'diffmap_evals', eigenvalues of transition matrix (adata.uns) (0:00:00)

sc.pl.umap(adata, color=["organoid","type","dataset","subLeiden","MKI67"], size = 100, add_outline = True,
           outline_width=(0.2, 0.05), ncols=3, save=nb_fname+".UMAP.pdf")

WARNING: saving figure to file figures/umapCycling01_Selection.UMAP.pdf

sc.pl.diffmap(adata, color=["organoid","type","dataset","subLeiden","MKI67","VIM","DCX","GAP43","PAX6","FOXG1","FGF8"], size = 50, add_outline = True,outline_width=(0.2, 0.05), ncols=3)

sc.pl.draw_graph(adata, color=["organoid","type","dataset","subLeiden","MKI67","VIM","DCX","GAP43","PAX6","FOXG1","FGF8"], size = 50, add_outline = True,outline_width=(0.2, 0.05), ncols=3)

sc.pl.draw_graphper type

sc.tl.embedding_density(adata, basis='umap', groupby='type')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_type')

computing density on 'umap'
--> added
    'umap_density_type', densities (adata.obs)
    'umap_density_type_params', parameter (adata.uns)

Plot per region

sc.tl.embedding_density(adata, basis='umap', groupby='region')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_region')

computing density on 'umap'
--> added
    'umap_density_region', densities (adata.obs)
    'umap_density_region_params', parameter (adata.uns)

Plot per organoid

sc.tl.embedding_density(adata, basis='umap', groupby='organoid')
sc.pl.embedding_density(adata, basis='umap', key='umap_density_organoid')

computing density on 'umap'
--> added
    'umap_density_organoid', densities (adata.obs)
    'umap_density_organoid_params', parameter (adata.uns)

adata.write_h5ad(outdir+FinalLeaf+"/4C_Cycling_DA.h5ad")